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Description

Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating opioid epidemic becomes a high priority for not only governments but also healthcare providers. This depends on critical knowledge to understand the risk of opioid overdose of patients. In this paper, we present our work on building machine learning based prediction models to predict opioid overdose of patients based on the history of patients’ electronic health records (EHR). We performed two studies using New York State claims data (SPARCS) with 440,000 patients and Cerner’s Health Facts database with 110,000 patients. Our experiments demonstrated that EHR based prediction can achieve best recall with random forest method (precision: 95.3%, recall: 85.7%, F1 score: 90.3%), best precision with deep learning (precision: 99.2%, recall: 77.8%, F1 score: 87.2%). We also discovered that clinical events are among critical features for the predictions.

Learning Objective: Understand data driven approach for opioid epidemic research;
Study machine learning including deep learning based approach for predictive modeling with Electronic Health Records.

Authors:

Xinyu Dong (Presenter)
Stony Brook University

Sina Rashidian, Stony Brook University
Yu Wang, Stony Brook University
Janos Hajagos, Stony Brook University
Xia Zhao, Stony Brook University
Richard Rosenthal, Stony Brook University
Jun Kong, Stony Brook University
Mary Saltz, Stony Brook University
Joel Saltz, Stony Brook University
Fusheng Wang, Stony Brook University

Presentation Materials:

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